1. Field of the Invention
The present invention relates generally to the field of network implementation, and more specifically to the field of network branch planning and placement.
2. Background of the Related Art
Whenever a company seeks to create or expand a service provider network consisting of branch locations by opening new stores, offices, and the like, the decision-makers must make several determinations. These include: how many branches to open; where to open them; and whether it is better to place a new branch within a populated area or between two or more populated areas.
Because of a lack of real data-gathering tools, traditional approaches to these questions generally may be reduced to one guideline: open a branch amongst the highest population of individuals possible where the company does not yet have a presence. This commonly used approach leaves unanswered many questions, including among others, whether a sprawling population will travel to a centrally based branch, and whether the traffic flow in the center of this area will take people away from the location.
By taking this simple approach, planners may well end up opening branches in unduly expensive areas, only to find that the population is doing its business elsewhere. Modern cities consist in large part of an inefficient network of roads, disparate population clusters, and assorted retail, commercial, industrial and residential areas. Consequently, the so-called “center of town” approach can leave planners with an expensive lease, no pass-by traffic, and the need to build yet another branch in another location.
Importantly, the “center of town” approach ignores significant, real-world, consumer behavior factors. For example, if 500,000 people live within the borders of City A, it could well be that another 300,000 live just outside of that city. The “center of town” approach may not effectively service that population. In other words, planners must consider whether the positioning of a “center of town” branch is going to service the satellite population.
Another common approach to the problem of network branch placement is to draw cartographically a circle of some radius around a center point in order to include other populations within the theoretical reach of a branch. While this approach may solve some problems associated with the “center of town” approach, it often introduces more errors and inaccuracies into the planning process. For example, consumers generally do not consider linear distances to branches when traveling (i.e., they do not think about traveling “as the crow flies”). Instead, consumers tend to think in terms of travel time (e.g., “driving time”), which may be affected by the presence or absence of: a direct route to the destination; freeways, highways and the like; areas of congestion; unusual traffic patterns; and traffic control devices such as traffic signals. Therefore, a potential customer who falls within an arbitrary cartographic circle may be further from a network branch in terms of travel time than another potential customer who falls outside the same circle.
Other current approaches may calculate travel time for potential populations; however, these approaches fail to assign any value to individual consumers or consumer populations and do not make adjustments to reflect travel time dependent probabilities nor any other factors which may affect network branch location value or consumer value. Such current approaches may be implemented using technology from Maplnfo Corporation, One Global View, Troy, N.Y. 12180; Magellan Ingenierie S. A., 710, Avenue Aristide Berges, 38330 Montbonnot, France; and InfoTech Enterprises Europe Ltd, Holborn Hall, 100 Gray's Inn Road, London WC1X 8AL, United Kingdom, for example.
With these considerations in mind, it is desirable to have a network branch placement tool which utilizes real world consumer behavior data to ascertain optimal placement of network branch locations.